Emerging Tools for Quantifying Unconscious Analgesia: Fractional-Order Impedance Models

This paper presents the application of model-based predictive control (MPC) in combination with a sensor for the measurement of analgesia (pain relief) in an unconscious patient in order to control the level of anesthesia. The MPC strategy uses fractional-order impedance models (FOIMs) to model the diffusion process that occurs in the human body when an analgesic drug is taken up. Based on this control strategy an early dawn concept of the pain sensor is developed. The grand challenges that coincide with this development include identification of the patient model, validation of the pain sensor, and validation of the effect of the analgesic drug.

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